Sweat sensing technologies have enormous potential for applications ranging from athletics, to neonates, to pharmacological monitoring, to personal digital health, to name a few applications. Sweat contains many of the same biomarkers, chemicals, or solutes that are carried in blood and can provide significant information enabling one to diagnose ailments, health status, toxins, performance, and other physiological attributes even in advance of any physical sign. Furthermore, sweat itself, the action of sweating, and other parameters, attributes, solutes, or features on, near, or beneath the skin can be measured to further reveal physiological information.
If sweat has such significant potential as a sensing paradigm, then why has it not emerged beyond decades-old usage in infant chloride assays for Cystic Fibrosis or in illicit drug monitoring patches? In decades of sweat sensing literature, the majority of medical literature utilizes the crude, slow, and inconvenient process of sweat stimulation, collection of a sample, transport of the sample to a lab, and then analysis of the sample by a bench-top machine and a trained expert. This process is so labor intensive, complicated, and costly that in most cases, one would just as well implement a blood draw since it is the gold standard for most forms of high performance biomarker sensing. Hence, sweat sensing has not emerged into its fullest opportunity and capability for biosensing, especially for continuous or repeated biosensing or monitoring. Furthermore, attempts at using sweat to sense “holy grails” such as glucose have not yet succeeded to produce viable commercial products, reducing the publically perceived capability and opportunity space for sweat sensing.
Of all the other physiological fluids used for bio monitoring (e.g. blood, urine, saliva, tears), sweat has arguably the most variable sampling rate as its collection methods and variable rate of generation both induce large variances in the effective sampling rate. Sweat is also exposed to numerous contamination sources, which can distort the effective sampling rate or concentrations. The variable sampling rate creates a challenge in providing chronological assurance, especially so in continuous monitoring applications.
For example, consider the difficulty of sampling sweat in a sweat sensing patch with a large sweat volume that could mix up sweat previously generated with the newly generated sweat that is intended to be measured to represent a measurement of sweat solutes in real time or near real time. Techniques exist to reduce the sweat volume, such as simply bringing standard sensors closer to skin, but even so the sweat volume is not completely eliminated. Furthermore, space between sweat glands contains the skin surface, which is not a source of sweat, therefore not contributing and of value to sweat sensing. Furthermore, the skin surface can cause contamination of the sweat signal by microbes on skin, by dead skin cell biomarkers, by contaminants on or in skin, or by diffusion of contaminates from the body to the skin surface.
Traditional methods of solving the above problem include those reported frequently in the clinical literature, such as coating the skin with petroleum jelly or oil through which sweat can push. However, these techniques have been demonstrated only for sweat collection and are not inherently compatible with a wearable sensor. For example, petroleum jelly would wet against the sensor and effectively seal it from any sweat. Furthermore, other possible sweat pressure activated techniques that are not made of gels or liquids must somehow be affixed to skin with a strategy that confines the sweat horizontally (such that it does not spread all over the skin surface, else sweat pressure activation is not possible). Conventional approaches will not work with wearable sensors, and inventive steps are required for enablement. Clearly, the state of art is lacking in inventions to properly reduce the volume between sensors and skin. Reducing sweat volume is critical for fast sampling times or sampling times having very low sweat rates, but also may be critical for prolonged stimulation (i.e., where less stimulation is required) and for improving biomarker measurements since, for many biomarkers, a low sweat rate is required to match biomarker concentrations in sweat to that found in blood.
Many of the drawbacks and limitations stated above can be resolved by creating novel and advanced interplays of chemicals, materials, sensors, electronics, microfluidics, algorithms, computing, software, systems, and other features or designs, in a manner that affordably, effectively, conveniently, intelligently, or reliably brings sweat sensing technology into intimate proximity with sweat as it is generated. With such a new invention, sweat sensing could become a compelling new paradigm as a biosensing platform.